Add script for extracting image tiles with reference images
This commit is contained in:
James Betker
parent
9963b37200
commit
57fc3f490c
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@ -9,6 +9,7 @@ import lmdb
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import pyarrow
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import torch.utils.data as data
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from tqdm import tqdm
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import torch
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def main():
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@ -19,15 +20,28 @@ def main():
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opt['compression_level'] = 90 # JPEG compression quality rating.
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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if mode == 'single':
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opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\images'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\lmdb_with_ref'
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opt['crop_sz'] = 512 # the size of each sub-image
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opt['step'] = 128 # step of the sliding crop window
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opt['dest'] = 'file'
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opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vixen\\full_video_segments'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vixen\\full_video_with_refs'
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opt['crop_sz'] = [256, 512, 1024] # the size of each sub-image
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opt['step'] = 256 # step of the sliding crop window
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opt['thres_sz'] = 128 # size threshold
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opt['resize_final_img'] = .5
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opt['resize_final_img'] = [1, .5, .25]
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opt['only_resize'] = False
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extract_single(opt, split_img)
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save_folder = opt['save_folder']
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if not osp.exists(save_folder):
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os.makedirs(save_folder)
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print('mkdir [{:s}] ...'.format(save_folder))
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if opt['dest'] == 'lmdb':
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writer = LmdbWriter(save_folder)
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else:
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writer = FileWriter(save_folder)
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extract_single(opt, writer, split_img)
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elif mode == 'pair':
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GT_folder = '../../datasets/div2k/DIV2K_train_HR'
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LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
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@ -91,7 +105,7 @@ class LmdbWriter:
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self.keys = []
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# Writes the given reference image to the db and returns its ID.
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def write_reference_image(self, ref_img):
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def write_reference_image(self, ref_img, _):
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id = self.ref_id
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self.ref_id += 1
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self.write_image(id, ref_img[0], ref_img[1])
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@ -123,6 +137,48 @@ class LmdbWriter:
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self.db.close()
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class FileWriter:
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def __init__(self, folder):
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self.folder = folder
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self.next_unique_id = 0
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self.ref_center_points = {} # Maps ref_img basename to a dict of image IDs:center points
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self.ref_ids_to_names = {}
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def get_next_unique_id(self):
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id = self.next_unique_id
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self.next_unique_id += 1
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return id
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def save_image(self, ref_path, img_name, img):
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save_path = osp.join(self.folder, ref_path)
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os.makedirs(save_path, exist_ok=True)
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f = open(osp.join(save_path, img_name), "wb")
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f.write(img)
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f.close()
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# Writes the given reference image to the db and returns its ID.
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def write_reference_image(self, ref_img, path):
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ref_img, _ = ref_img # Encoded with a center point, which is irrelevant for the reference image.
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img_name = osp.basename(path).replace(".jpg", "").replace(".png", "")
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self.ref_center_points[img_name] = {}
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self.save_image(img_name, "ref.jpg", ref_img)
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id = self.get_next_unique_id()
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self.ref_ids_to_names[id] = img_name
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return id
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# Writes a tile image to the db given a reference image and returns its ID.
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def write_tile_image(self, ref_id, tile_image):
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id = self.get_next_unique_id()
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ref_name = self.ref_ids_to_names[ref_id]
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img, center = tile_image
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self.ref_center_points[ref_name][id] = center
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self.save_image(ref_name, "%08i.jpg" % (id,), img)
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return id
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def close(self):
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for ref_name, cps in self.ref_center_points.items():
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torch.save(cps, osp.join(self.folder, ref_name, "centers.pt"))
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class TiledDataset(data.Dataset):
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def __init__(self, opt, split_mode=False):
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self.split_mode = split_mode
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@ -136,16 +192,48 @@ class TiledDataset(data.Dataset):
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else:
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return self.get(index, False, False)
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def get(self, index, split_mode, left_img):
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path = self.images[index]
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crop_sz = self.opt['crop_sz']
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def get_for_scale(self, img, split_mode, left_img, crop_sz, resize_factor):
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step = self.opt['step']
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thres_sz = self.opt['thres_sz']
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only_resize = self.opt['only_resize']
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h, w, c = img.shape
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if split_mode:
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w = w/2
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h_space = np.arange(0, h - crop_sz + 1, step)
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if h - (h_space[-1] + crop_sz) > thres_sz:
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h_space = np.append(h_space, h - crop_sz)
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w_space = np.arange(0, w - crop_sz + 1, step)
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if w - (w_space[-1] + crop_sz) > thres_sz:
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w_space = np.append(w_space, w - crop_sz)
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index = 0
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tile_dim = int(crop_sz * resize_factor)
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dsize = (tile_dim, tile_dim)
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results = []
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for x in h_space:
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for y in w_space:
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index += 1
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crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
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center_point = (x + crop_sz // 2, y + crop_sz // 2)
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crop_img = np.ascontiguousarray(crop_img)
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if 'resize_final_img' in self.opt.keys():
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# Resize too.
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center_point = (int(center_point[0] * resize_factor), int(center_point[1] * resize_factor))
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crop_img = cv2.resize(crop_img, dsize, interpolation=cv2.INTER_AREA)
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success, buffer = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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assert success
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results.append((buffer, center_point))
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return results
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def get(self, index, split_mode, left_img):
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path = self.images[index]
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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# We must convert the image into a square. Crop the image so that only the center is left, since this is often
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# the most salient part of the image.
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if len(img.shape) == 2: # Greyscale not supported.
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return None
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h, w, c = img.shape
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dim = min(h, w)
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img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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@ -153,7 +241,7 @@ class TiledDataset(data.Dataset):
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h, w, c = img.shape
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# Uncomment to filter any image that doesnt meet a threshold size.
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if min(h,w) < 1024:
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return
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return None
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left = 0
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right = w
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if split_mode:
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@ -163,48 +251,20 @@ class TiledDataset(data.Dataset):
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else:
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left = int(w/2)
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right = w
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w = int(w/2)
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img = img[:, left:right]
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h_space = np.arange(0, h - crop_sz + 1, step)
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if h - (h_space[-1] + crop_sz) > thres_sz:
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h_space = np.append(h_space, h - crop_sz)
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w_space = np.arange(0, w - crop_sz + 1, step)
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if w - (w_space[-1] + crop_sz) > thres_sz:
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w_space = np.append(w_space, w - crop_sz)
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tile_dim = int(self.opt['crop_sz'][0] * self.opt['resize_final_img'][0])
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dsize = (tile_dim, tile_dim)
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dsize = None
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if only_resize:
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dsize = (crop_sz, crop_sz)
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if h < w:
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h_space = [0]
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w_space = [(w - h) // 2]
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crop_sz = h
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else:
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h_space = [(h - w) // 2]
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w_space = [0]
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crop_sz = w
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index = 0
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resize_factor = self.opt['resize_final_img'] if 'resize_final_img' in self.opt.keys() else 1
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dsize = (int(crop_sz * resize_factor), int(crop_sz * resize_factor))
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# Reference image should always be first.
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results = [(cv2.resize(img, dsize, interpolation=cv2.INTER_AREA), (-1,-1))]
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for x in h_space:
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for y in w_space:
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index += 1
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crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
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center_point = (x + crop_sz // 2, y + crop_sz // 2)
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crop_img = np.ascontiguousarray(crop_img)
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if 'resize_final_img' in self.opt.keys():
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# Resize too.
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resize_factor = self.opt['resize_final_img']
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center_point = (int(center_point[0] * resize_factor), int(center_point[1] * resize_factor))
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crop_img = cv2.resize(crop_img, dsize, interpolation=cv2.INTER_AREA)
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success, buffer = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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# Reference image should always be first entry in results.
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ref_img = cv2.resize(img, dsize, interpolation=cv2.INTER_AREA)
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success, ref_buffer = cv2.imencode(".jpg", ref_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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assert success
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results.append((buffer, center_point))
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return results
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results = [(ref_buffer, (-1,-1))]
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for crop_sz, resize_factor in zip(self.opt['crop_sz'], self.opt['resize_final_img']):
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results.extend(self.get_for_scale(img, split_mode, left_img, crop_sz, resize_factor))
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return results, path
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def __len__(self):
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return len(self.images)
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@ -213,23 +273,20 @@ class TiledDataset(data.Dataset):
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def identity(x):
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return x
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def extract_single(opt, split_img=False):
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save_folder = opt['save_folder']
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if not osp.exists(save_folder):
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os.makedirs(save_folder)
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print('mkdir [{:s}] ...'.format(save_folder))
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lmdb = LmdbWriter(save_folder)
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def extract_single(opt, writer, split_img=False):
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dataset = TiledDataset(opt, split_img)
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dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
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tq = tqdm(dataloader)
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for imgs in tq:
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if imgs is None or imgs[0] is None:
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continue
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imgs, path = imgs[0]
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if imgs is None or len(imgs) <= 1:
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continue
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ref_id = lmdb.write_reference_image(imgs[0])
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ref_id = writer.write_reference_image(imgs[0], path)
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for tile in imgs[1:]:
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lmdb.write_tile_image(ref_id, tile)
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lmdb.close()
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writer.write_tile_image(ref_id, tile)
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writer.close()
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if __name__ == '__main__':
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